Articles tagged with Artificial Neural Networks
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Researchers at UVA have created a new AI tool called SITHCon that can decode speech in different speeds, enabling machines to process information more efficiently. This breakthrough technology has the potential to alter how artificial neural networks 'think' and reduce AI's massive carbon footprint.
Researchers at NIST have developed a new type of hardware for AI that uses magnetic tunnel junctions, which are less energy-intensive than traditional silicon chips. The new technology has already passed a virtual wine-tasting test and shows promise for reducing energy use in AI systems.
Researchers from Osaka University found that facial similarity plays a crucial role in ratings of trustworthiness for observers of the same sex, but not for observers of opposite sex. The study suggests that facial similarity is an important factor affecting social judgments for same-sex interactions.
Researchers developed a novel convolutional neural network for facial expression recognition, outperforming conventional models while being computationally less expensive. The new model achieved an accuracy of 72.4% using only 58,000 parameters.
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
Researchers from the University of Tsukuba develop a model that combines multiple theories to simulate motor learning in humans. The study found that larger amounts of motor exploration aid in learning sensitivity derivatives and transforming errors into motor corrections.
A new AI system uses artificial neural networks to recognize objects more accurately and stably, despite changing visual inputs. The system mimics human eye movements to improve machine vision capabilities, reducing errors in self-driving cars and other applications.
A new AI-powered mental health application, FuturSelf, uses machine learning to identify the shortest path to mental stability. The system offers personalized recommendations for improving long-term well-being.
Researchers use machine learning to automatically analyze Reflection High-Energy Electron Diffraction (RHEED) data, enabling faster and more efficient discovery of new materials. The study focused on surface superstructures in thin-film silicon surfaces and identified optimal synthesis conditions using non-negative matrix factorization.
Researchers have developed an AI-powered approach to calculate molecular spectra using Graph Neural Networks (GNNs), significantly reducing computation time and improving accuracy. The SchNet model achieved a 20% increase in accuracy while reducing computational time, enabling the analysis of complex molecules like quantum dots.
The Department of Energy has awarded Early Career Research Program funding to three Oak Ridge National Laboratory scientists. The awardees will receive $500,000 annually for five years to support their research in fusion energy, advanced scientific computing, and biogeochemical controls on phosphorus cycling.
A new training algorithm for deep spiking neural networks (SNNs) uses biologically plausible spatiotemporal adjustment to improve performance and reduce energy consumption. This approach achieves competitive classification accuracy with only 3% of the energy used by traditional artificial neural networks.
A team from Nagoya University created an artificial neural network model that performed the delayed matching-to-sample task and analyzed its behavior. The model was able to evolve to exhibit human-like metamemory, adapting to its environment by learning and evolving. This breakthrough aims to create machines with memories like humans.
A study by HBP scientists found that wakefulness, non-REM sleep, and REM sleep have complementary functions for learning: experiencing stimuli, solidifying experiences, and discovering semantic concepts. This research suggests that unusual dreams, simulated using Generative Adversarial Networks, can improve brain learning by introducin...
The university's new Robotics and Autonomous Systems Teaching and Innovation Center (RASTIC) will provide students with hands-on experience in robotics, autonomous systems, and self-driving technology. The lab aims to boost Massachusetts' competitiveness in the tech sector by supporting innovative projects and startups.
Artificial Intelligence can now identify legendary batting techniques used by Sir Donald Bradman and modern players. Researchers developed a deep learning computer vision AI model to detect lateral backlift batters from straight ones.
Researchers at the University of Missouri are applying AI to analyze protein dynamics, identifying potential target sites for new drug therapies. The approach can simulate protein changes related to conditions like cancer, enhancing the chances of successful therapies.
MIT researchers develop ExSum, a framework to formalize explanations of machine-learning models into quantifiable rules. This allows for testing assumptions about model behavior and reveals unexpected insights, such as negative words having sharper contributions to model decisions.
Researchers have developed a method using nanomagnets to perform artificial intelligence, slashing energy costs and offering huge efficiency gains. The technology uses 'nanomagnetic states' to process and store data, cutting out the need for software simulation.
Scientists at the University of Oxford have developed an 'optomemristor' device that facilitates three-factor learning and emulation of biological computations, making it possible to perform complex machine learning tasks. The device uses both light and electrical signals to interact and consume very little energy.
A study found that trainee teachers who received AI-generated feedback improved their diagnostic reasoning, identifying potential learning difficulties in pupils more accurately. The AI system analyzed the trainees' work and provided clear, adaptive feedback.
A new AI-based approach can predict cardiac arrest with significant accuracy, identifying patients at risk and predicting the likelihood of sudden cardiac death. The technology stands to transform clinical decision-making and increase survival rates from lethal arrhythmias.
Researchers have developed a new method called Shared Interest that enables users to aggregate, sort, and rank individual explanations of a machine-learning model's reasoning. This technique uses quantifiable metrics to compare how well the model's reasoning matches human thinking, helping to uncover concerning trends in decision-making.
Researchers found that AI-enhanced diagnosis helps doctors accurately detect fetal congenital heart disease, with fellows making the most accurate diagnoses. The new system uses graphical charts to represent the AI's analysis of ultrasound videos, improving accuracy and trust among medical professionals.
Researchers at MIT developed a framework for robotic manipulation systems that can perform complex tasks using a two-stage learning process. This allows robots to learn abstract ideas about manipulating deformable objects, such as pizza dough, and execute skills to complete tasks.
Adversarially robust models capture aspects of human peripheral processing, with results showing similarity in image transformations and perception alignment. The study's findings shed light on the goals of peripheral processing in humans and could help improve machine learning models.
A new framework for portfolio management uses deep reinforcement learning to predict price trends and make strategic decisions, overcoming limitations of existing systems. The system consists of evolving agent modules and strategic agent modules, allowing for modular design and scalability.
GIST researchers propose a new strategy for crime prevention using artificial intelligence, trained on a large-scale dataset of deviant incident reports and corresponding images. The model, called DevianceNet, can accurately classify and detect deviant places, making it a useful tool in urban safety development.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
Researchers at Tokyo Institute of Technology have developed a new AI processor called Hiddenite, which achieves state-of-the-art accuracy in sparse neural networks with lower computational burdens. The chip drastically reduces external memory access for enhanced computational efficiency.
A team of researchers from Skoltech and universities developed a neural network-based solution for automated recognition of chemical formulas on research paper scans. The algorithm combines molecules, functional groups, fonts, styles, and printing defects to mimic existing molecular template depiction styles.
Anastasios Kyrillidis has won a National Science Foundation CAREER Award to explore the theory and design of non-convex optimization algorithms. His research aims to devise algorithmic foundations and theory that will accelerate problem-solving in machine learning, information processing, and optimization.
Researchers at Purdue University have created a device that can dynamically rewire itself to adapt to new data, enabling artificial intelligence to learn and remember information like the human brain. This breakthrough could lead to more efficient AI systems for tasks such as image recognition and decision-making.
A new technique uses compression to reduce data transmission size, allowing for efficient federated learning on wireless devices. The approach has been shown to condense data packets by up to 99%, making it suitable for areas with limited bandwidth.
A team of researchers from the Institute of Industrial Science, The University of Tokyo, used a mathematical model to examine the implications of intergenerational learning. They found that learning accelerated the evolutionary process, which may assist in designing more efficient hybrid algorithms.
Researchers at KTH Royal Institute of Technology and Stanford University have developed a material that enables the commercial viability of neuromorphic computers mimicking the human brain. The material, MXene, combines high speed, temperature stability, and integration compatibility in a single device.
A team of scientists developed an AI-based model to predict personal thermal comfort based on spatial parameters, achieving exceptional accuracy. The study highlights the importance of incorporating architectural features in models to reduce energy consumption.
A team at the University of Washington has created an optical computing system that not only reduces noise but also utilizes it to improve creative output. The system uses a Generative Adversarial Network and demonstrates the viability of this technology at a large scale.
MIT researchers develop a method to test feature-attribution methods for machine-learning models. They find that even the most popular methods often miss important features in an image and some perform as poorly as a random baseline. This has major implications for high-stakes situations like medical diagnoses.
Researchers at the University of Surrey have successfully demonstrated the use of multimodal transistors in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations. The study paves the way for thin-film decision and classification circuits, which could be used in more complex AI ...
Researchers at RIKEN CBS demonstrate that neural networks minimize energy cost and solve mazes efficiently, pointing to a set of universal mathematical rules. The findings will aid in analyzing impaired brain function and generating optimized neural networks for artificial intelligences.
A team of researchers from Osaka University has developed a simple system based on electrochemical reactions that can perform complex calculations. The system uses polyoxometalate molecules and deionized water to process information and solve nonlinear problems.
A research team at SUTD has developed an ultra-scalable artificial synapse using 2D materials, enabling the commercialization of brain-inspired hardware. The device integrates functional and silent synapses into a single unit, reducing hardware costs and improving efficiency.
Researchers at MIT and Google Brain developed a system that predicts how changing materials or designs will improve solar cell performance. The new simulator, called differentiable solar cell simulator, provides information on which changes will provide desired improvements, increasing the rate of discovery of new configurations.
A team of scientists has created a neural network that can predict and generate new protein structures using deep learning. The network, trained on random protein sequences, can produce stable protein shapes with remarkable accuracy.
Scientists at TU Wien have developed a novel germanium-based transistor with the ability to perform different logical tasks, offering improved adaptability and flexibility in chip design. This technology has potential applications in artificial intelligence, neural networks, and logic circuits that work with more than just 0 and 1.
A team of researchers, including those from Rensselaer Polytechnic Institute and the University of Washington, have developed a neural network that can predict protein shapes with high accuracy. The network was trained on random protein sequences and generated 2,000 new proteins, many of which were successfully produced in the lab.
A new confocal platform using artificial intelligence and multiple lenses improves volumetric resolution by over 10-fold while reducing phototoxicity. The platform uses Deep Learning algorithms to distinguish between high-quality images with low signal-to-noise ratio and better images.
Researchers discovered the retrosplenial cortex as the site of value decision-making in the brain. Persistency allows value signals to be effectively represented across different brain areas, especially the RSC. Artificial intelligence networks mimicking mouse decisions showed remarkably similar results.
A new study by USC researchers uses GANs to generate synthetic neurological data that can be fed into machine-learning algorithms to improve BCI usability. This approach improved BCI training speed by up to 20 times and enabled rapid adaptation to new subjects.
Researchers trained an artificial intelligence algorithm to predict the next designer drugs before they are even on the market, allowing law enforcement agencies to identify and regulate new versions of dangerous psychoactive drugs. The model was tested against 196 new designer drugs and found nearly all were present in its generated set.
A new machine learning-based approach enhances student engagement in online environments. The algorithm detects when students disengage, prompting interventions to improve learning outcomes.
A new study explores the problem of shortcuts in a popular machine learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data. By removing simpler characteristics and asking the model to solve the task two ways, researchers reduce the tendency for shortcut solutions and boost performance.
A new algorithm has been developed to train spiking neural networks, mimicking the human brain's structure and function. This approach enables these powerful, fast, and energy-efficient systems to solve complex tasks like image classification with high precision.
Researchers have trained a neural network to detect anomalies in medical images, adapting it to the nature of medical imaging and achieving better results. The new method uses weakly supervised training and can spot small-scale anomalies, accelerating the work of histopathologists and radiologists.
Researchers at Osaka University developed a deep neural network to accurately determine qubit states despite environmental noise. The novel approach may lead to more robust and practical quantum computing systems.
Researchers developed an attention-based deep neural network to detect multiple ship targets, exceeding conventional networks' performance. The model focused on inherent features of the two ships simultaneously, outperforming traditional approaches.
A team of scientists at NAIST successfully used automatic differentiation to accelerate calculations of model parameter extraction, reducing computation time by 3.5 times compared to conventional methods. This breakthrough enables the design of more efficient power converters with increased performance and reduced energy consumption.
A team of researchers from the University of Illinois Urbana-Champaign used advanced machine learning to model the physico-chemical properties of a molten salt compound called FLiNaK, enabling accurate atomic-scale reproduction and prediction of behavior under specific reactor conditions. This computational framework can help character...
Researchers at Singapore University of Technology and Design (SUTD) have designed an ultralow power artificial synapse for next-generation AI systems. The team's innovation uses a nanoscale deposit-only-metal-electrode fabrication process, achieving an all-time-low energy consumption of 1.8 pJ per pair-pulse-based synaptic event.